Chen Liang (梁宸)
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Hi! This is Chen, a computer science Ph.D. candidate from Pervasive Computing Group at Tsinghua University, supervised by Prof. Yuanchun Shi and Prof. Chun Yu. I received my Bachelor's degree in Computer Science from Tsinghua University in 2019. My major research direction is Human-Computer Interaction (HCI) and I am also keeping track of research updates on natural language processing and computer vision.

My research interest focuses on facilitating natural and efficient interaction schemes with compact sensor form in XR and mobile scenarios by leveraging multi-modality sensing (e.g., vision, audio, inertial signal, and RF). My previous and ongoing work expanded the user's input capability both in spatial (e.g., enabling precise input on the subtle fingertip unintrusively) and temporal (e.g., enhancing the recognition of fast and transient gestures) domains. My goal is to develop fundamental interaction techniques, with which a user can interact with everything around them seamlessly, for the next generation XR interface (just like the mouse and the keyboard for GUI).

Recent News:

1. Great pleasure to give a talk at HKUST(GZ). Thank Prof. Mingming Fan for the invitation.

2. One paper have been conditionally accepted by IMWUT (May 2023). Congratulations to my collaborators!

3. One paper has been conditionally accepted by UIST 2023 (pending minor revision). Congratulations to my collaborators!

4. Our paper "Enabling Voice-Accompanying Hand-to-Face Gesture Recognition with Cross-Device Sensing" has received the Honorable Mention Award of CHI 2023! Congratulations to Zisu!

Chen Liang from Department of Computer Scinece at Tsinghua University


UIST 2023
ShadowTouch: Enabling Free-Form Touch-Based Hand-to-Surface Interaction with Wrist-Mounted Illuminant by Shadow Projection
Chen Liang, Xutong Wang, Zisu Li, Chi Hsia, Mingming Fan, Chun Yu, and Yuanchun Shi

We present ShadowTouch, a novel sensing method to recognize the subtle hand-to-surface touch state for independent fingers based on optical auxiliary. ShadowTouch mounts a forward-facing light source on the user's wrist to construct shadows on the surface in front of the fingers when the corresponding fingers are close to the surface. With such an optical design, the subtle vertical movements of near-surface fingers are magnified and turned to shadow features cast on the surface, which are recognizable for computer vision algorithms. To efficiently recognize the touch state of each finger, we devised a two-stage CNN-based algorithm that first extracted all the fingertip regions from each frame and then classified the touch state of each region from the cropped consecutive frames. Evaluations showed our touch state detection algorithm achieved a recognition accuracy of 99.1% and an F-1 score of 96.8% in the leave-one-out cross-user evaluation setting. We further outlined the hand-to-surface interaction space enabled by ShadowTouch's sensing capability from the aspects of touch-based interaction, stroke-based interaction, and out-of-surface information and developed four application prototypes to showcase ShadowTouch's interaction potential. The usability evaluation study showed the advantages of ShadowTouch over threshold-based techniques in aspects of lower mental demand, lower effort, lower frustration, more willing to use, easier to use, better integrity, and higher confidence.

IMWUT 2023
Exploring the Opportunities of AR for Enriching Storytelling with Family Photos between Grandparents and Grandchildren
Zisu Li, Li Feng, Chen Liang, Yuru Huang, and Mingming Fan

Storytelling with family photos, as an important mode of reminiscence-based activities, can be instrumental in promoting intergenerational communication between grandparents and grandchildren by strengthening generation bonds and shared family values. Motivated by challenges that existing technology approaches encountered for improving intergenerational storytelling (e.g., the need to hold the tablet, the potential view detachment from the physical world in Virtual Reality (VR)), we sought to find new ways of using Augmented Reality (AR) to support intergenerational storytelling, which offers new capabilities (e.g., 3D models, new interactivity) to enhance the expression for the storyteller. We conducted a two-part exploratory study, where pairs of grandparents and grandchildren 1) participated in an in-person storytelling activity with a semi-structured interview 2) and then a participatory design session with AR technology probes that we designed to inspire their exploration. Our findings revealed insights into the possible ways of intergenerational storytelling, the feasibility and usages of AR in facilitating it, and the key design implications for leveraging AR in intergenerational storytelling.

IMWUT 2023
Understanding In Situ Programming for Smart Home Automation
* indicates equal contribution.
Xiaoyi Liu*, Yingtian Shi*, Chun Yu, Cheng Gao, Tianao Yang, Chen Liang, and Yuanchun Shi

Programming a smart home is an iterative process in which users configure and test the automation during the in-situ experience with IoT space. However, current end-user programming mechanisms are primarily preset configurations on GUI and fail to leverage in-situ behaviors and context. This paper proposed in-situ programming (ISP) as a novel programming paradigm for AIoT automation that extensively leverages users' natural in-situ interaction with the smart environment. We built a Wizard-of-Oz system and conducted a user-enactment study to explore users' behavior models in this paradigm. We identified a dynamic programming flow in which participants iteratively configure and confirm through query, control, edit, and test. We especially identified a novel method ``snapshot'' for automation configuration and a novel method ``simulation'' for automation testing, in which participants leverage ambient responses and in-situ interaction. Based on our findings, we proposed design spaces on dynamic programming flow, coherency and clarity of interface, and state and scene management to build an ideal in-situ programming experience.

IMWUT 2023
From 2D to 3D: Facilitating Single-Finger Mid-Air Typing on Virtual Keyboards with Probabilistic Touch Modeling
* indicates equal contribution.
Xin Yi, Chen Liang*, Haozhan Chen*, Jiuxu Song, Chun Yu, Hewu Li, and Yuanchun Shi

Mid-air text entry on virtual keyboards suffers from the lack of tactile feedback, which brings challenges to both tap detection and input prediction. In this paper, we explored the feasibility of single-finger typing on virtual QWERTY keyboards in mid-air. We first conducted a study to examine users’ 3D typing behavior on different sizes of virtual keyboards. Results showed that the participants perceived the vertical projection of the lowest point on the keyboard during a tap as the target location and inferring taps based on the intersection between the finger and the keyboard was not applicable. Aiming at this challenge, we derived a novel input prediction algorithm that took the uncertainty in tap detection into a calculation as probability, and performed probabilistic decoding that could tolerate false detection ...

CHI 2023
Enabling Voice-Accompanying Hand-to-Face Gesture Recognition with Cross-Device Sensing (Honorable Mention Award)
Zisu Li*, Chen Liang*, Yuntao Wang, Yue Qin, Chun Yu, Yukang Yan, Mingming Fan, Yuanchun Shi
* indicates equal contribution.

Gestures performed accompanying the voice are essential for voice interaction to convey complementary semantics for interaction purposes such as wake-up state and input modality. In this paper, we investigated voice-accompanying hand-to-face (VAHF) gestures for voice interaction. We targeted hand-to-face gestures because such gestures relate closely with speech and yield significant acoustic features (e.g., impeding voice propagation). We conducted a user study to explore the design space of VAHF gestures, where we gathered candidate gestures and then applied a structural analysis to them in different dimensions (e.g., contact position and type), outputting a total of 8 VAHF gestures with good usability and least confusion. To facilitate VAHF gesture recognition, we proposed a novel cross-device sensing method that leverages heterogeneous data channels (vocal, ultrasound, and IMU) from commodity devices.

CHI 2023
Selecting Real-World Objects via User-Perspective Phone Occlusion
Yue Qin, Chun Yu, Wentao Yao, Jiachen Yao, Chen Liang, Yueting Weng, Yukang Yan, and Yuanchun Shi

Perceiving the region of interest (ROI) and target object by smartphones from the user's first-person perspective can enable diverse spatial interactions. In this paper, we propose a novel ROI input method and a target selecting method for smartphones by utilizing the user-perspective phone occlusion. This concept of turning the phone into real-world physical cursor benefits from the proprioception, gets rid of the constraint of camera preview, and allows users to rapidly and accurately select the target object ...

IMWUT 2022
DRG-Keyboard: Enabling Subtle Gesture Typing on the Fingertip with Dual IMU Rings
Chen Liang, Chi Hsia, Chun Yu, Yukang Yan, Yuntao Wang, and Yuanchun Shi

We present DRG-Keyboard, a gesture keyboard enabled by dual IMU rings, allowing the user to swipe the thumb on the index fingertip to perform word gesture typing as if typing on a miniature QWERTY keyboard. With dual IMUs attached to the user’s thumb and index finger, DRG-Keyboard can 1) measure the relative attitude while mapping it to the 2D fingertip coordinates and 2) detect the thumb’s touch-down and touch-up events combining the relative attitude data and the synchronous frequency domain data, based on which a fingertip gesture keyboard can be implemented ...

IMWUT 2021
DualRing: Enabling Subtle and Expressive Hand Interaction with Dual IMU Rings
Chen Liang, Chun Yu, Yue Qin, Yuntao Wang, and Yuanchun Shi

We present DualRing, a novel ring-form input device that can capture the state and movement of the user's hand and fingers. With two IMU rings attached to the user's thumb and index finger, DualRing can sense not only the absolute hand gesture relative to the ground but also the relative pose and movement among hand segments. To enable natural thumb-to-finger interaction, we develop a high-frequency AC circuit for on-body contact detection ...
[PDF] [Video]

CHI 2021
Auth+Track: Enabling Authentication Free Interaction on Smartphone by Continuous User Tracking
Chen Liang, Chun Yu, Xiaoying Wei, Xuhai Xu, Yongquan Hu, Yuntao Wang, Yuanchun Shi

We propose Auth+Track, a novel authentication model that reduces redundant authentication in everyday smartphone usage. By sparse authentication and continuous tracking of the user’s status, Auth+Track eliminates the “gap” authentication between fragmented sessions and enables “Authentication Free when User is Around”. To instantiate the Auth+Track model, we present PanoTrack, a prototype that integrates body and near field hand information for user tracking. We install a fisheye camera on the top of the phone to achieve a panoramic vision that can capture both user’s body and on-screen hands ...
[PDF] [Video]

CHI 2019
HandSee: Enabling Full Hand Interaction on Smartphones with Front Camera-based Stereo Vision
Yu Chun, Xiaoying Wei, Shubh Vachher, Yue Qin, Chen Liang, Yueting Weng, Yizheng Gu, Yuanchun Shi

We present HandSee, a novel sensing technique that can capture the state of the user’s hands touching or gripping a smartphone. We place a prism mirror on the front camera to achieve a stereo vision of the scene above the touchscreen surface. HandSee enables a variety of novel interaction techniques and expands the design space for full hand interaction on smartphones...
[PDF] [Video]

AAAI 2019
Deepchannel: Salience estimation by contrastive learning for extractive document summarization
Jiaxin Shi*, Chen Liang*, Lei Hou, Juanzi Li, Zhiyuan Liu, and Hanwang Zhang
* indicates equal contribution.

We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization. Given any document-summary pair, we estimate a salience score, which is modeled using an attention-based deep neural network, to represent the salience degree of the summary for yielding the document. We devise a contrastive training strategy to learn the salience estimation network, and then use the learned salience score as a guide and iteratively extract the most salient sentences from the document as our generated summary ...

Posters and Demos

VR 2022 Workshop
From 2D to 3D: Facilitating Single-Finger Mid-Air Typing on Virtual Keyboards with Probabilistic Touch Modeling
Xin Yi, Chen Liang, Haozhan Chen, Jiuxu Song, Chun Yu, and Yuanchun Shi

Mid-air text entry on virtual keyboards suffers from the lack of tactile feedback, bringing challenges to both tap detection and input prediction. In this poster, we demonstrated the feasibility of efficient single-finger typing in mid-air through probabilistic touch modeling. We first collected users' typing data on different sizes of virtual keyboards. Based on analyzing the data, we derived an input prediction algorithm that incorporated probabilistic touch detection and elastic probabilistic decoding. In the evaluation study where the participants performed real text entry tasks with this technique, they reached a pick-up single-finger typing speed of 24.0 WPM with 2.8% word-level error rate.
[PDF] [Video]


Tsinghua University 09/2019 - present
Ph.D. student in Computer Science and Technology

Tsinghua University 09/2015 - 06/2019
B.Eng. in Computer Science and Technology

National University of Singapore 07/2018 - 09/2018
Research Intern

Honors & Awards

CHI Honorable Mention Award (Top 5%) 2023
84 Innovation and Future Scholarship (Top 6), Tsinghua University 2021
Excellent Comprehensive Scholarship, Tsinghua University 2021, 2022
The Second Prize of the 39th "Challenge Cup", Tsinghua University 2021
The First Prize of the 37th "Challenge Cup", Tsinghua University 2019
Excellent Graduate of Computer Science, Tsinghua University 2019


Reviewer, CHI 2020, TURC 2020, CHI LBW 2021, ACL 2021, CHI 2022, TIOT 2022 2019 -

Teaching Assistant (TA), Fundamentals of Computer Programming 2019 Fall, 2020 Fall, 2021 Fall, 2022 Fall

Teaching Assistant (TA), Essentials to Signal Processing and Data Management for AIoT Applications 2022 Fall

Teaching Assistant (TA), Financial Big Data and Quantitative Analysis 2021 Spring

Teaching Assistant (TA), Calculus A(2) 2020 Spring

Invited Talks

Towards Ubiquitous and Intelligent Hand Interaction 07/2023
HKUST(GZ), Host: Prof. Mingming Fan


Programming Language: C/C++, Python, Java, JavaScript, CUDA, R, Matlab, VHDL, Assembly Language

Professional Skills: Machine Learning (CV, NLP, and RL tasks in Pytorch and TensorFlow), Digital Image Processing, Audio Processing, Embedded System (e.g., Arduino, NRF BLE)

Updated in Jun. 2023